Kim Soopil, Park Yae Won, Park Sang Hyun, Ahn Sung Soo, Chang Jong Hee, Kim Se Hoon, Lee Seung Koo
Department of Robotics Engineering, Daegu Gyeongbuk Institute of Science and Technology, Daegu, Korea.
Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea.
Brain Tumor Res Treat. 2020 Apr;8(1):36-42. doi: 10.14791/btrt.2020.8.e3.
To compare the diagnostic performance of two-dimensional (2D) and three-dimensional (3D) fractal dimension (FD) and lacunarity features from MRI for predicting the meningioma grade.
This retrospective study included 123 meningioma patients [90 World Health Organization (WHO) grade I, 33 WHO grade II/III] with preoperative MRI including post-contrast T1-weighted imaging. The 2D and 3D FD and lacunarity parameters from the contrast-enhancing portion of the tumor were calculated. Reproducibility was assessed with the intraclass correlation coefficient. Multivariable logistic regression analysis using 2D or 3D fractal features was performed to predict the meningioma grade. The diagnostic ability of the 2D and 3D fractal models were compared.
The reproducibility between observers was excellent, with intraclass correlation coefficients of 0.97, 0.95, 0.98, and 0.96 for 2D FD, 2D lacunarity, 3D FD, and 3D lacunarity, respectively. WHO grade II/III meningiomas had a higher 2D and 3D FD (=0.003 and <0.001, respectively) and higher 2D and 3D lacunarity (=0.002 and =0.006, respectively) than WHO grade I meningiomas. The 2D fractal model showed an area under the curve (AUC), accuracy, sensitivity, and specificity of 0.690 [95% confidence interval (CI) 0.581-0.799], 72.4%, 75.8%, and 64.4%, respectively. The 3D fractal model showed an AUC, accuracy, sensitivity, and specificity of 0.813 (95% CI 0.733-0.878), 82.9%, 81.8%, and 70.0%, respectively. The 3D fractal model exhibited significantly better diagnostic performance than the 2D fractal model (<0.001).
The 3D fractal analysis proved superiority in diagnostic performance to 2D fractal analysis in grading meningioma.
比较二维（2D）和三维（3D）分形维数（FD）以及磁共振成像（MRI）的 lacunarity 特征在预测脑膜瘤分级方面的诊断性能。
这项回顾性研究纳入了 123 例脑膜瘤患者[90 例世界卫生组织（WHO）一级，33 例 WHO 二级/三级]，这些患者术前行 MRI 检查，包括增强后 T1 加权成像。计算肿瘤增强部分的 2D 和 3D FD 以及 lacunarity 参数。用组内相关系数评估可重复性。使用 2D 或 3D 分形特征进行多变量逻辑回归分析以预测脑膜瘤分级。比较 2D 和 3D 分形模型的诊断能力。
观察者之间的可重复性极佳，2D FD、2D lacunarity、3D FD 和 3D lacunarity 的组内相关系数分别为 0.97、0.95、0.98 和 0.96。WHO 二级/三级脑膜瘤的 2D 和 3D FD（分别为=0.003 和<0.001）以及 2D 和 3D lacunarity（分别为=0.002 和=0.006）均高于 WHO 一级脑膜瘤。2D 分形模型的曲线下面积（AUC）、准确性、敏感性和特异性分别为 0.690[95%置信区间（CI）0.581 - 0.799]、72.4%、75.8%和 64.4%。3D 分形模型的 AUC、准确性、敏感性和特异性分别为 0.813（95%CI 0.733 - 0.878）、82.9%、81.8%和 70.0%。3D 分形模型的诊断性能明显优于 2D 分形模型（<0.001）。
在脑膜瘤分级中，3D 分形分析在诊断性能上优于 2D 分形分析。
